CommuNety: A Deep Learning System for the Prediction of Cohesive Social Communities

TL;DR

This paper introduces CommuNety, a deep learning system that predicts cohesive social communities from images, utilizing hierarchical CNNs, face co-occurrence analysis, and photo ranking to outperform existing methods.

Contribution

It presents a novel deep learning framework combining hierarchical CNNs with face co-occurrence and photo ranking algorithms for social community prediction from images.

Findings

Superior performance on PIPA dataset compared to state-of-the-art methods

Effective prediction of relationships between individuals in images

Accurate assessment of community cohesiveness

Abstract

Effective mining of social media, which consists of a large number of users is a challenging task. Traditional approaches rely on the analysis of text data related to users to accomplish this task. However, text data lacks significant information about the social users and their associated groups. In this paper, we propose CommuNety, a deep learning system for the prediction of cohesive social networks using images. The proposed deep learning model consists of hierarchical CNN architecture to learn descriptive features related to each cohesive network. The paper also proposes a novel Face Co-occurrence Frequency algorithm to quantify existence of people in images, and a novel photo ranking method to analyze the strength of relationship between different individuals in a predicted social network. We extensively evaluate the proposed technique on PIPA dataset and compare with state-of-the-art methods. Our experimental results demonstrate the superior performance of the proposed technique for the prediction of relationship between different individuals and the cohesiveness of communities.